An Object-Oriented Model of Simulation for Internet-Based Multi-Operator Multi-Robot System

2013 ◽  
Vol 401-403 ◽  
pp. 1738-1741
Author(s):  
Sheng Gao ◽  
Yang Zhang ◽  
Dong Dong Chen
Keyword(s):  
Object Oriented ◽  
The Other ◽  
Robot System ◽  
Unified Modeling ◽  
System A ◽  
Multi Robot

This paper describes an object-oriented model of simulation for multi-operator multi-robot system. A universal hierarchies and inheritances framework and messages passing patterns are proposed, which supply a unified modeling interface with reusability and extensibility. Developers can concentrate on the other related issues of application rather than programming issues.

scholarly journals A Multi-robot System Coordination Design and Analysis on Wall Follower Robot Group

2018 ◽  
Vol 8 (6) ◽  
pp. 5098
Author(s):  
Agung Nugroho Jati ◽  
Randy Erfa Saputra ◽  
M. Ghozy Nurcahyadi ◽  
Nasy'an Taufiq Al Ghifary
Keyword(s):  
The Other ◽  
Communication Process ◽  
Time Process ◽  
Output Process ◽  
Robot System ◽  
Large Space ◽  
Ultrasound Sensor ◽  
The Right ◽  
Robot Group ◽  
Multi Robot

In this research, multi-robot formation can be established according to the environment or workspace. Group of robots will move sequently if there is no space for robots to stand side by side. Leader robot will be on the front of all robots and follow the right wall. On the other hand, robots will move side by side if there is a large space between them. Leader robot will be tracked the wall on its right side and follow on it while every follower moves side by side. The leader robot have to broadcast the information to all robots in the group in radius 9 meters. Nevertheless, every robot should be received information from leader robot to define their movements in the area. The error provided by fuzzy output process which is caused by read data from ultrasound sensor will drive to more time process. More sampling can reduce the error but it will drive more execution time. Furthermore, coordination time will need longer time and delay. Formation will not be establisehed if packet error happened in the communication process because robot will execute wrong command.

Dynamic model based formation control and obstacle avoidance of multi-robot systems

Robotica ◽  
2008 ◽  
Vol 26 (3) ◽  
pp. 345-356 ◽  
Author(s):  
Celso De La Cruz ◽  
Ricardo Carelli
Keyword(s):  
Dynamic Model ◽  
Obstacle Avoidance ◽  
Formation Control ◽  
Inverse Dynamics ◽  
Single Equation ◽  
Model Parameters ◽  
Robot System ◽  
System A ◽  
Robot Systems ◽  
Multi Robot

SUMMARYThis work presents, first, a complete dynamic model of a unicycle-like mobile robot that takes part in a multi-robot formation. A linear parameterization of this model is performed in order to identify the model parameters. Then, the robot model is input-output feedback linearized. On a second stage, for the multi-robot system, a model is obtained by arranging into a single equation all the feedback linearized robot models. This multi-robot model is expressed in terms of formation states by applying a coordinate transformation. The inverse dynamics technique is then applied to design a formation control. The controller can be applied both to positioning and to tracking desired robot formations. The formation control can be centralized or decentralized and scalable to any number of robots. A strategy for rigid formation obstacle avoidance is also proposed. Experimental results validate the control system design.

scholarly journals Emotional model for a multi-robot system with emergent behavior

2020 ◽  
Vol 9 (3) ◽  
pp. 220
Author(s):  
Angel Gil ◽  
Jose Aguilar ◽  
Eladio Dapena ◽  
Rafael Rivas
Keyword(s):  
Emotional State ◽  
General Purpose ◽  
Emergent Behavior ◽  
The Other ◽  
Collective Actions ◽  
Robot System ◽  
Basic Emotions ◽  
Emotional Process ◽  
Emotional Model ◽  
Multi Robot

<p>This article describes an emotional model for a general-purpose robot operating in a multi-robot system with emergent behavior. The model considers four basic emotions: anger, rejection, sadness and joy, plus  a neutral emotional state, which affect the behavior of the robot,  both individually and collectively. The emotional state of each robot in  the system is constructed through the conjunction of a series of factors related to their individual and collective actions, which are: safety, load, acting and interaction, which serve as input to an emotional process that results in an index of satisfaction of the robot that establishes the emotional state in which it is in a certain moment. The emotional state of a robot influences its interactions with the other robots and with the environment, that is, it determines its emergent behavior in the system. This paper  presents the design of this model, and establishes some considerations for its implementation.</p>

On the Weighting of Control Signals in a Multi-robot System: A Formation-Based Analysis

2020 ◽  
Vol 31 (5) ◽  
pp. 1121-1131
Author(s):  
Valentim Ernandes-Neto ◽  
Gabriel V. Pacheco ◽  
Alexandre S. Brandão
Keyword(s):  
Robot System ◽  
System A ◽  
Control Signals ◽  
Multi Robot

Fuzzy coordination through measure information sharing of multi-robot system: A case study

2021 ◽  
pp. 1-8
Author(s):  
Xuefeng Dai ◽  
Jiazhi Wang ◽  
Dahui Li ◽  
Yanchun Wang
Keyword(s):  
Robot System ◽  
Fuzzy Membership Functions ◽  
System A ◽  
Parameter Update Law ◽  
Robot Systems ◽  
Potential Applications ◽  
The Individual ◽  
Logic Reasoning ◽  
Multi Robot

Multi-robot systems have many potential applications; however, the available results for coordination were based on qualitative information. Fuzzy logic reasoning has a feature of human being thinking, so a novel coordinated algorithm is proposed. The algorithm utilizes sharing sensing information of rooms and semantic robots to coordinating robots in a structured environment exploration. The approach divides all teammate robots into two classes according to robot exploration performance, and divides rooms into large, medium and small ones according to estimations of the individual areas. On the purpose of minimizing exploration time of the system, the reasoning coordination assigns large room to good performance robot, and vice versa. A parameter update law is introduced for fuzzy membership functions. Finally, the results are validated by computer simulations for a structured environment.

Exploration Strategies of Coordinated Multi-Robot System: A Comparative Study

2018 ◽  
Vol 7 (1) ◽  
pp. 48
Author(s):  
Ayman. El shenawy ◽  
Khalil. Mohamed ◽  
Hany. M. Harb
Keyword(s):  
Comparative Study ◽  
Process Performance ◽  
Basic Process ◽  
Robot System ◽  
Exploration Process ◽  
Coordination Strategy ◽  
System A ◽  
Environment Exploration ◽  
Robot Systems ◽  
Multi Robot

Environment Exploration is the basic process that most of Multi Robot Systems applications depend on it. The exploration process performance depends on the coordination strategy between the robots participating in the team.  In this paper the coordination of Multi Robot Systems in the exploration process is surveyed, and the performance of different Multi Robot Systems exploration strategies is contrasted and analyzed for different environments and different team sizes.

Comparing Plume-Tracing Algorithms in Obstructed Multi-Source Environments

2013 ◽  
Vol 756-759 ◽  
pp. 228-232
Author(s):  
Yu Li Zhang ◽  
Xiao Ping Ma
Keyword(s):  
Search Performance ◽  
Robot System ◽  
System A ◽  
Plume Tracing ◽  
Environment Simulation ◽  
Cooperation Strategy ◽  
The Common ◽  
Robot Cooperation ◽  
Dynamics Simulations ◽  
Multi Robot

In this paper, we compare the common plume-tracing algorithms: chemotaxis and anemotaxis in obstructed multi-source environment using multi-robot system. A multi-robot cooperation strategy with virtual physics force, which includes structure formation force, goal force, obstacle avoidance force, repulsion force and rotary force, is proposed. First, plume model with two sources in three obstacles environment is constructed by computation fluid dynamics simulations. Second, parallel searches by two groups robots with chemotaxis and anemotaxis are used to locate two sources in obstructed environment. Simulation comparison experiment with two plume-tracing algorithms discussed the influence of the varied wind direction/ speed frequency and methane release frequency and different initial positions of two groups to the search performance. Finally, the comparative result is illustrated.

Cooperative Multi-Robot Object Tracking in Unknown Environment Using Covariance Intersection

2013 ◽  
Vol 631-632 ◽  
pp. 1101-1105
Author(s):  
Ming Wu ◽  
Lin Lin Li ◽  
Wei Zhen Hua
Keyword(s):  
Object Tracking ◽  
Point Of View ◽  
The Other ◽  
Fusion Method ◽  
Extend Kalman Filter ◽  
Robot System ◽  
Unknown Environment ◽  
Surrounding Environment ◽  
Covariance Intersection ◽  
Multi Robot

This work presents a approach for multiple cooperating mobile robots for moving object tracking in unknown environment. Each robot in the team uses the full covariance extend Kalman filter based algorithm to simultaneously localize the robot and target while building a landmark feature map of the surrounding environment. Meanwhile, in local robot system the covariance intersection based data fusion method is used to fuse information sent by the other robot teammates, those information may contains the location of target and the location of robot itself from other teammate’s point of view. The method is distributed, and let the multi-robot system have the ability of robustness. The results of simulation validate a higher accuracy of our method compared with non-fusion single robot solution.

scholarly journals Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning

2019 ◽  
Vol 9 (20) ◽  
pp. 4198
Author(s):  
Wenzhou Chen ◽  
Shizheng Zhou ◽  
Zaisheng Pan ◽  
Huixian Zheng ◽  
Yong Liu
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Gradient Algorithm ◽  
Lidar Data ◽  
Robot System ◽  
Navigation Task ◽  
System A ◽  
Group Navigation ◽  
Policy Gradient ◽  
Multi Robot

Compared with the single robot system, a multi-robot system has higher efficiency and fault tolerance. The multi-robot system has great potential in some application scenarios, such as the robot search, rescue and escort tasks, and so on. Deep reinforcement learning provides a potential framework for multi-robot formation and collaborative navigation. This paper mainly studies the collaborative formation and navigation of multi-robots by using the deep reinforcement learning algorithm. The proposed method improves the classical Deep Deterministic Policy Gradient (DDPG) to address the single robot mapless navigation task. We also extend the single-robot Deep Deterministic Policy Gradient algorithm to the multi-robot system, and obtain the Parallel Deep Deterministic Policy Gradient (PDDPG). By utilizing the 2D lidar sensor, the group of robots can accomplish the formation construction task and the collaborative formation navigation task. The experiment results in a Gazebo simulation platform illustrates that our method is capable of guiding mobile robots to construct the formation and keep the formation during group navigation, directly through raw lidar data inputs.

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